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                        Introduction
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                            <b>1.</b>
                        
                        第一章：数据分析前奏
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                            <b>1.1.</b>
                        
                        第1节：什么是数据分析
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                            <b>1.2.</b>
                        
                        第2节：环境搭建
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                            <b>1.3.</b>
                        
                        第3节：jupyternotebook使用
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                            <b>1.4.</b>
                        
                        第4节：作业
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                            <b>2.</b>
                        
                        第二章：Numpy库
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                            <b>2.1.</b>
                        
                        第1节：numpy库介绍
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                            <b>2.2.</b>
                        
                        第2节：numpy数组基本
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                            <b>2.3.</b>
                        
                        第3节：numpy数组操作
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                            <b>2.4.</b>
                        
                        第4节：numpy索引和切片
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                            <b>2.5.</b>
                        
                        第5节：Numpy索引和切片作业
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                            <b>2.6.</b>
                        
                        第6节：numpy数组操作
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                            <b>2.7.</b>
                        
                        第7节：深拷贝和浅拷贝
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                            <b>2.8.</b>
                        
                        第8节：文件操作
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                            <b>2.9.</b>
                        
                        第9节：数组操作和文件操作作业.md
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                            <b>2.10.</b>
                        
                        第10节：NAN和INF值处理.md
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                            <b>2.11.</b>
                        
                        第11节：random模块.md
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                            <b>2.12.</b>
                        
                        第12节：axis理解.md
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                            <b>2.13.</b>
                        
                        第13节：通用函数.md
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                            <b>2.14.</b>
                        
                        第14节：numpy练习题.md
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                            <b>3.</b>
                        
                        第三章：Pandas库
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                            <b>3.1.</b>
                        
                        第1节：pandas介绍
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                            <b>3.2.</b>
                        
                        第2节：pandas索引操作
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                            <b>3.3.</b>
                        
                        第3节：pandas对齐运算
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                            <b>3.4.</b>
                        
                        第4节：pandas函数应用
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                            <b>3.5.</b>
                        
                        第5节：pandas层级索引
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                            <b>3.6.</b>
                        
                        第6节：pandas统计计算和描述
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                            <b>3.7.</b>
                        
                        第7节：文件操作
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                            <b>3.8.</b>
                        
                        第8节：数据清洗
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                            <b>3.9.</b>
                        
                        第9节：聚合和分组
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                            <b>3.10.</b>
                        
                        第10节：时间序列
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                            <b>4.</b>
                        
                        第四章：Matploblib库
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                            <b>4.1.</b>
                        
                        第1节：常用图
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                            <b>4.2.</b>
                        
                        第2节：基本使用
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                            <b>4.3.</b>
                        
                        第3节：条形图
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                            <b>4.4.</b>
                        
                        第4节：直方图
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                            <b>4.5.</b>
                        
                        第5节：散点图
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                            <b>4.6.</b>
                        
                        第6节：饼图
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                            <b>4.7.</b>
                        
                        第7节：箱线图
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                            <b>4.8.</b>
                        
                        第8节：雷达图
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                            <b>4.9.</b>
                        
                        第9节：matplotlib绘图分析
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                            <b>4.10.</b>
                        
                        第10节：多图布局
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                            <b>4.11.</b>
                        
                        第11节：matplotlib配置
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                            <b>4.12.</b>
                        
                        第12节：matplotlib作业
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                            <b>5.</b>
                        
                        第五章：Seaborn库
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                            <b>5.1.</b>
                        
                        第1节：关系绘图
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                            <b>5.2.</b>
                        
                        第2节：分类绘图
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                            <b>5.3.</b>
                        
                        第3节：分布绘图
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                            <b>5.4.</b>
                        
                        第4节：线性关系绘图
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                            <b>5.5.</b>
                        
                        第5节：FacetGrid结构图
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                            <b>5.6.</b>
                        
                        第6节：样式设置
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                            <b>5.7.</b>
                        
                        第7节：调色盘设置
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                            <b>5.8.</b>
                        
                        第8节：seaborn作业
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                            <b>6.</b>
                        
                        第六章：统计分析强化
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                            <b>6.1.</b>
                        
                        第1节：常用专业数学术语
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                            <b>6.2.</b>
                        
                        第2节：平均数
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                            <b>6.3.</b>
                        
                        第3节：方差和标准差
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                            <b>6.4.</b>
                        
                        第4节：正态分布
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                            <b>6.5.</b>
                        
                        第5节：对比分析
                    </a>
            
            
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        <li class="chapter " data-level="6.6" data-path="chapter3/06分布分析.html">
            
                
                    <a href="../chapter3/06分布分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>6.6.</b>
                        
                        第6节：分布分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="6.7" data-path="chapter3/07交叉分析.html">
            
                
                    <a href="../chapter3/07交叉分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>6.7.</b>
                        
                        第7节：交叉分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="6.8" data-path="chapter3/08统计分析.html">
            
                
                    <a href="../chapter3/08统计分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>6.8.</b>
                        
                        第8节：统计分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="6.9" data-path="chapter3/09帕累托分析.html">
            
                
                    <a href="../chapter3/09帕累托分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>6.9.</b>
                        
                        第9节：帕累托分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="6.10" data-path="chapter3/10矩阵关联分析.html">
            
                
                    <a href="../chapter3/10矩阵关联分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>6.10.</b>
                        
                        第10节：矩阵关联分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="6.11" data-path="chapter3/11综合性分析.html">
            
                
                    <a href="../chapter3/11综合性分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>6.11.</b>
                        
                        第11节：综合性分析
                    </a>
            
            
        </li>
    

            </ul>
            
        </li>
    
        <li class="chapter " data-level="7" data-path="chapter6/00.html">
            
                
                    <a href="../chapter6/00.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>7.</b>
                        
                        第七章：数据分析实战
                    </a>
            
            
            <ul class="articles">
                
    
        <li class="chapter " data-level="7.1" data-path="chapter6/01App数据分析实战.html">
            
                
                    <a href="../chapter6/01App数据分析实战.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>7.1.</b>
                        
                        第1节：App数据分析实战
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="7.2" data-path="chapter6/02心脏病患者数据分析.html">
            
                
                    <a href="../chapter6/02心脏病患者数据分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>7.2.</b>
                        
                        第2节：心脏病患者数据分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="7.3" data-path="chapter6/03StackoverFlow数据分析.html">
            
                
                    <a href="../chapter6/03StackoverFlow数据分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>7.3.</b>
                        
                        第3节：StackoverFlow数据分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="7.4" data-path="chapter6/04二手房数据分析.html">
            
                
                    <a href="../chapter6/04二手房数据分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>7.4.</b>
                        
                        第4节：二手房数据分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="7.5" data-path="chapter6/05吃鸡数据分析.html">
            
                
                    <a href="../chapter6/05吃鸡数据分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>7.5.</b>
                        
                        第5节：吃鸡数据分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="7.6" data-path="chapter6/06黑色星期五数据分析.html">
            
                
                    <a href="../chapter6/06黑色星期五数据分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>7.6.</b>
                        
                        第6节：黑色星期五数据分析
                    </a>
            
            
        </li>
    

            </ul>
            
        </li>
    
        <li class="chapter " data-level="8" data-path="chapter7/00.html">
            
                
                    <a href="../chapter7/00.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>8.</b>
                        
                        第八章：补充
                    </a>
            
            
            <ul class="articles">
                
    
        <li class="chapter " data-level="8.1" data-path="chapter7/01用Excel做数据分析.html">
            
                
                    <a href="../chapter7/01用Excel做数据分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>8.1.</b>
                        
                        第1节：用Excel做数据分析
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="8.2" data-path="chapter7/02echarts和pyecharts库.html">
            
                
                    <a href="../chapter7/02echarts和pyecharts库.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>8.2.</b>
                        
                        第2节：echarts和pyecharts库
                    </a>
            
            
        </li>
    
        <li class="chapter " data-level="8.3" data-path="chapter7/03bokeh库.html">
            
                
                    <a href="../chapter7/03bokeh库.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>8.3.</b>
                        
                        第3节：bokeh库
                    </a>
            
            
        </li>
    

            </ul>
            
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                        <h1 id="&#x73AF;&#x5883;&#x642D;&#x5EFA;">&#x73AF;&#x5883;&#x642D;&#x5EFA;</h1>
<h2 id="&#x4E00;&#x3001;python&#x7248;&#x672C;&#xFF1A;">&#x4E00;&#x3001;Python&#x7248;&#x672C;&#xFF1A;</h2>
<p>&#x672C;&#x8BFE;&#x7A0B;&#x7528;&#x5230;&#x7684;Python&#x7248;&#x672C;&#x90FD;&#x662F;3.x&#x3002;&#x8981;&#x6709;&#x4E00;&#x5B9A;&#x7684;Python&#x57FA;&#x7840;&#xFF0C;&#x77E5;&#x9053;&#x5217;&#x8868;&#x3001;&#x5B57;&#x7B26;&#x4E32;&#x3001;&#x51FD;&#x6570;&#x7B49;&#x7684;&#x7528;&#x6CD5;&#x3002;</p>
<hr>
<h2 id="anaconda&#xFF1A;">Anaconda&#xFF1A;</h2>
<p><code>Anaconda&#xFF08;&#x6C34;&#x87D2;&#xFF09;</code>&#x662F;&#x4E00;&#x4E2A;&#x6346;&#x7ED1;&#x4E86;<code>Python</code>&#x3001;<code>conda</code>&#x3001;&#x5176;&#x4ED6;&#x76F8;&#x5173;&#x4F9D;&#x8D56;&#x5305;&#x7684;&#x4E00;&#x4E2A;&#x8F6F;&#x4EF6;&#x3002;&#x5305;&#x542B;&#x4E86;180&#x591A;&#x4E2A;&#x53EF;&#x5B66;&#x8BA1;&#x7B97;&#x5305;&#x53CA;&#x5176;&#x4F9D;&#x8D56;&#x3002;<code>Anaconda3</code>&#x662F;&#x96C6;&#x6210;&#x4E86;<code>Python3</code>&#x7684;&#x73AF;&#x5883;&#xFF0C;<code>Anaconda2</code>&#x662F;&#x96C6;&#x6210;&#x4E86;<code>Python2</code>&#x7684;&#x73AF;&#x5883;&#x3002;<code>Anaconda</code>&#x9ED8;&#x8BA4;&#x96C6;&#x6210;&#x7684;&#x5305;&#xFF0C;&#x662F;&#x5C5E;&#x4E8E;&#x5185;&#x7F6E;&#x7684;<code>Python</code>&#x7684;&#x5305;&#x3002;&#x5E76;&#x4E14;&#x652F;&#x6301;&#x7EDD;&#x5927;&#x90E8;&#x5206;&#x64CD;&#x4F5C;&#x7CFB;&#x7EDF;&#xFF08;&#x6BD4;&#x5982;&#xFF1A;Windows&#x3001;Mac&#x3001;Linux&#x7B49;&#xFF09;&#x3002;&#x4E0B;&#x8F7D;&#x5730;&#x5740;&#x5982;&#x4E0B;&#xFF1A;<code>https://www.anaconda.com/distribution/</code>&#xFF08;&#x5982;&#x679C;&#x5B98;&#x7F51;&#x4E0B;&#x8F7D;&#x592A;&#x6162;&#xFF0C;&#x53EF;&#x4EE5;&#x5728;&#x6E05;&#x534E;&#x5927;&#x5B66;&#x5F00;&#x6E90;&#x8F6F;&#x4EF6;&#x7AD9;&#x4E2D;&#x4E0B;&#x8F7D;&#xFF1A;<code>https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/</code>&#xFF09;&#x3002;&#x6839;&#x636E;&#x81EA;&#x5DF1;&#x7684;&#x64CD;&#x4F5C;&#x7CFB;&#x7EDF;&#xFF0C;&#x4E0B;&#x8F7D;&#x76F8;&#x5E94;&#x7684;&#x7248;&#x672C;&#xFF0C;&#x56E0;&#x4E3A;<code>Anaconda</code>&#x5185;&#x7F6E;&#x4E86;&#x8BB8;&#x591A;&#x7684;&#x5305;&#xFF0C;&#x6240;&#x4EE5;&#x5B89;&#x88C5;&#x8FC7;&#x7A0B;&#x9700;&#x8981;&#x8017;&#x8D39;&#x76F8;&#x5F53;&#x957F;&#x7684;&#x65F6;&#x95F4;&#xFF0C;&#x5927;&#x5BB6;&#x5728;&#x5B89;&#x88C5;&#x7684;&#x65F6;&#x5019;&#x9700;&#x8981;&#x8010;&#x5FC3;&#x7B49;&#x5F85;&#x3002;&#x5728;&#x5B89;&#x88C5;&#x5B8C;&#x6210;&#x540E;&#xFF0C;&#x4F1A;&#x6709;&#x4EE5;&#x4E0B;&#x51E0;&#x4E2A;&#x6A21;&#x5757;&#xFF1A;<code>Anaconda prompt</code>&#x3001;<code>Anaconda Navigator</code>&#x3001;<code>Spyder</code>&#x3001;<code>jupyter notebook</code>&#xFF0C;&#x4EE5;&#x4E0B;&#x5206;&#x522B;&#x505A;&#x4E00;&#x4E9B;&#x4ECB;&#x7ECD;&#x3002;</p>
<h3 id="anaconda-prompt&#xFF1A;">Anaconda prompt&#xFF1A;</h3>
<p><code>Anaconda prompt</code>&#x662F;&#x4E13;&#x95E8;&#x7528;&#x6765;&#x64CD;&#x4F5C;<code>anaconda</code>&#x7684;&#x7EC8;&#x7AEF;&#x3002;&#x5982;&#x679C;&#x4F60;&#x5B89;&#x88C5;&#x5B8C;<code>Anaconda</code>&#x540E;&#x6CA1;&#x6709;&#x5728;&#x73AF;&#x5883;&#x53D8;&#x91CF;&#x7684;<code>PATH</code>&#x4E2D;&#x6DFB;&#x52A0;&#x76F8;&#x5173;&#x7684;&#x73AF;&#x5883;&#x53D8;&#x91CF;&#xFF0C;&#x90A3;&#x4E48;&#x4EE5;&#x540E;&#x4F60;&#x60F3;&#x5728;&#x7EC8;&#x7AEF;&#x4F7F;&#x7528;<code>anaconda</code>&#x76F8;&#x5173;&#x7684;&#x547D;&#x4EE4;&#xFF0C;&#x5219;&#x5FC5;&#x987B;&#x8981;&#x5728;<code>Anaconda prompt</code>&#x4E2D;&#x5B8C;&#x6210;&#x3002;<br><img src="../assets/QQ&#x622A;&#x56FE;20190215154246.png" alt=""></p>
<h3 id="anaconda-navigator&#xFF1A;">Anaconda Navigator&#xFF1A;</h3>
<p>&#x8FD9;&#x4E2A;&#x76F8;&#x5F53;&#x4E8E;&#x662F;&#x4E00;&#x4E2A;&#x5BFC;&#x822A;&#x9762;&#x677F;&#xFF0C;&#x4E0A;&#x9762;&#x7EC4;&#x7EC7;&#x4E86;<code>Anaconda</code>&#x76F8;&#x5173;&#x7684;&#x8F6F;&#x4EF6;&#x3002;</p>
<h3 id="spyder&#xFF1A;">Spyder&#xFF1A;</h3>
<p>&#x4E00;&#x4E2A;&#x4E13;&#x95E8;&#x5F00;&#x53D1;<code>Python</code>&#x7684;&#x8F6F;&#x4EF6;&#xFF0C;&#x719F;&#x6089;<code>MATLAB</code>&#x7684;&#x540C;&#x5B66;&#x4F1A;&#x6BD4;&#x8F83;&#x6709;&#x4EB2;&#x5207;&#x611F;&#xFF0C;&#x4F46;&#x5728;&#x540E;&#x671F;&#x7684;&#x5B66;&#x4E60;&#x8FC7;&#x7A0B;&#x4E2D;&#xFF0C;&#x6211;&#x4EEC;&#x5C06;&#x4E0D;&#x4F1A;&#x4F7F;&#x7528;&#x8FD9;&#x4E2A;&#x5DE5;&#x5177;&#x5199;&#x4EE3;&#x7801;&#xFF0C;&#x56E0;&#x4E3A;&#x8FD8;&#x6709;&#x66F4;&#x597D;&#x7684;&#x53EF;&#x66FF;&#x4EE3;&#x7684;&#x5DE5;&#x5177;&#x3002;<br><img src="../assets/QQ&#x622A;&#x56FE;20190215155346.png" alt=""></p>
<h3 id="jupyter-notebook&#xFF1A;">jupyter notebook&#xFF1A;</h3>
<p>&#x4E00;&#x4E2A;Python&#x7F16;&#x8F91;&#x73AF;&#x5883;&#xFF0C;&#x53EF;&#x4EE5;&#x5B9E;&#x65F6;&#x7684;&#x67E5;&#x770B;&#x4EE3;&#x7801;&#x7684;&#x8FD0;&#x884C;&#x6548;&#x679C;&#x3002;</p>
<h2 id=""><img src="../assets/QQ&#x622A;&#x56FE;20190215155935.png" alt=""></h2>
<h2 id="&#x4F7F;&#x7528;jupyter-notebook&#x7684;&#x59FF;&#x52BF;&#xFF1A;">&#x4F7F;&#x7528;jupyter notebook&#x7684;&#x59FF;&#x52BF;&#xFF1A;</h2>
<ol>
<li>&#x5148;&#x6253;&#x5F00;<code>Anaconda Prompt</code>&#xFF0C;&#x7136;&#x540E;&#x8FDB;&#x5165;&#x5230;&#x9879;&#x76EE;&#x6240;&#x5728;&#x7684;&#x76EE;&#x5F55;&#x3002;</li>
<li>&#x8F93;&#x5165;&#x547D;&#x4EE4;<code>jupyter notebook</code>&#x6253;&#x5F00;<code>jupyter notebook</code>&#x6D4F;&#x89C8;&#x5668;&#x3002;</li>
</ol>
<h2 id="conda&#x57FA;&#x672C;&#x4F7F;&#x7528;&#xFF1A;">conda&#x57FA;&#x672C;&#x4F7F;&#x7528;&#xFF1A;</h2>
<p><code>conda</code>&#x4F34;&#x968F;&#x7740;<code>Anaconda</code>&#x5B89;&#x88C5;&#x800C;&#x81EA;&#x52A8;&#x5B89;&#x88C5;&#x7684;&#x3002;<code>conda</code>&#x53EF;&#x4EE5;&#x8DDF;<code>virtualenv</code>&#x4E00;&#x6837;&#x7BA1;&#x7406;&#x4E0D;&#x540C;&#x7684;&#x73AF;&#x5883;&#xFF0C;&#x4E5F;&#x53EF;&#x4EE5;&#x8DDF;<code>pip</code>&#x4E00;&#x6837;&#x7BA1;&#x7406;&#x67D0;&#x4E2A;&#x73AF;&#x5883;&#x4E0B;&#x7684;&#x5305;&#x3002;&#x4EE5;&#x4E0B;&#x6765;&#x770B;&#x770B;&#x4E24;&#x4E2A;&#x529F;&#x80FD;&#x7684;&#x7528;&#x6CD5;&#x3002;</p>
<h3 id="&#x73AF;&#x5883;&#x7BA1;&#x7406;&#xFF1A;">&#x73AF;&#x5883;&#x7BA1;&#x7406;&#xFF1A;</h3>
<p><code>conda</code>&#x80FD;&#x8DDF;<code>virtualenv</code>&#x4E00;&#x6837;&#x7BA1;&#x7406;&#x4E0D;&#x540C;&#x7684;<code>Python</code>&#x73AF;&#x5883;&#xFF0C;&#x4E0D;&#x540C;&#x7684;&#x73AF;&#x5883;&#x4E4B;&#x95F4;&#x662F;&#x4E92;&#x76F8;&#x9694;&#x79BB;&#xFF0C;&#x4E92;&#x4E0D;&#x5F71;&#x54CD;&#x7684;&#x3002;&#x4E3A;&#x4EC0;&#x4E48;&#x9700;&#x8981;&#x521B;&#x5EFA;&#x4E0D;&#x540C;&#x7684;&#x73AF;&#x5883;&#x5462;&#xFF1F;&#x539F;&#x56E0;&#x662F;&#x6709;&#x65F6;&#x5019;&#x9879;&#x76EE;&#x6BD4;&#x8F83;&#x591A;&#xFF0C;&#x4F46;&#x662F;&#x9879;&#x76EE;&#x4F9D;&#x8D56;&#x7684;&#x5305;&#x4E0D;&#x4E00;&#x6837;&#xFF0C;&#x6BD4;&#x5982;<code>A</code>&#x9879;&#x76EE;&#x7528;&#x7684;&#x662F;<code>Python2</code>&#x5F00;&#x53D1;&#x7684;&#xFF0C;&#x800C;<code>B</code>&#x9879;&#x76EE;&#x7528;&#x7684;&#x662F;<code>Python3</code>&#x5F00;&#x53D1;&#x7684;&#xFF0C;&#x90A3;&#x4E48;&#x6211;&#x4EEC;&#x5728;&#x540C;&#x4E00;&#x53F0;&#x7535;&#x8111;&#x4E0A;&#x5C31;&#x9700;&#x8981;&#x4E24;&#x5957;&#x4E0D;&#x540C;&#x7684;&#x73AF;&#x5883;&#x6765;&#x652F;&#x6491;&#x4ED6;&#x4EEC;&#x8FD0;&#x884C;&#x4E86;&#x3002;&#x521B;&#x5EFA;&#x73AF;&#x5883;&#x7684;&#x57FA;&#x672C;&#x547D;&#x4EE4;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-shell"># conda create --name [&#x73AF;&#x5883;&#x540D;&#x79F0;] &#x6BD4;&#x5982;&#x4EE5;&#x4E0B;&#xFF1A;
conda create --name da-env
</code></pre>
<p>&#x8FD9;&#x6837;&#x5C06;&#x521B;&#x5EFA;&#x4E00;&#x4E2A;&#x53EB;&#x505A;<code>da-env</code>&#x7684;&#x73AF;&#x5883;&#xFF0C;&#x8FD9;&#x4E2A;&#x73AF;&#x5883;&#x7684;<code>python</code>&#x89E3;&#x91CA;&#x5668;&#x6839;&#x636E;<code>anaconda</code>&#x6765;&#xFF0C;&#x5982;&#x679C;<code>anaconda</code>&#x4E3A;<code>3.7</code>&#xFF0C;&#x90A3;&#x4E48;&#x5C06;&#x9ED8;&#x8BA4;&#x4F7F;&#x7528;<code>3.7</code>&#x7684;&#x73AF;&#x5883;&#xFF0C;&#x5982;&#x679C;<code>anaconda</code>&#x5185;&#x7F6E;&#x7684;&#x662F;<code>2.7</code>&#xFF0C;&#x90A3;&#x4E48;&#x5C06;&#x9ED8;&#x8BA4;&#x4F7F;&#x7528;<code>2.7</code>&#x7684;&#x73AF;&#x5883;&#x3002;&#x7136;&#x540E;&#x4F60;&#x5C31;&#x53EF;&#x4EE5;&#x4F7F;&#x7528;<code>conda install numpy</code>&#x7684;&#x65B9;&#x5F0F;&#x6765;&#x5B89;&#x88C5;&#x5305;&#x4E86;&#xFF0C;&#x5E76;&#x4E14;&#x8FD9;&#x6837;&#x5B89;&#x88C5;&#x8FDB;&#x6765;&#x7684;&#x5305;&#xFF0C;&#x53EA;&#x4F1A;&#x5B89;&#x88C5;&#x5728;&#x5F53;&#x524D;&#x73AF;&#x5883;&#x4E2D;&#x3002;&#x6709;&#x7684;&#x540C;&#x5B66;&#x53EF;&#x80FD;&#x6709;&#x60F3;&#x95EE;&#xFF0C;&#x5982;&#x679C;&#x60F3;&#x8981;&#x88C5;&#x4E00;&#x4E2A;<code>Python2.7</code>&#x7684;&#x73AF;&#x5883;&#xFF0C;<code>anaconda</code>&#x4E2D;&#x6CA1;&#x6709;&#x5185;&#x7F6E;<code>Python2.7</code>&#xFF0C;&#x90A3;&#x4E48;&#x8BE5;&#x600E;&#x4E48;&#x5B9E;&#x73B0;&#x5462;&#xFF1F;&#x3002;&#x5B9E;&#x9645;&#x4E0A;&#xFF0C;&#x6211;&#x4EEC;&#x53EA;&#x9700;&#x8981;&#x5728;&#x5B89;&#x88C5;&#x7684;&#x65F6;&#x5019;&#x6307;&#x5B9A;<code>python</code>&#x7684;&#x7248;&#x672C;&#xFF0C;&#x5982;&#x679C;&#x8FD9;&#x4E2A;&#x7248;&#x672C;&#x73B0;&#x5728;&#x4E0D;&#x5B58;&#x5728;&#xFF0C;&#x90A3;&#x4E48;<code>anaconda</code>&#x4F1A;&#x81EA;&#x52A8;&#x7684;&#x7ED9;&#x6211;&#x4EEC;&#x4E0B;&#x8F7D;&#x3002;&#x6240;&#x4EE5;&#x5B89;&#x88C5;<code>Python2.7</code>&#x7684;&#x73AF;&#x5883;&#xFF0C;&#x4F7F;&#x7528;&#x4EE5;&#x4E0B;&#x4EE3;&#x7801;&#x5373;&#x53EF;&#x5B9E;&#x73B0;&#xFF1A;</p>
<pre><code>conda create --name xxx python=2.7
</code></pre><p>&#x4EE5;&#x4E0B;&#x518D;&#x5217;&#x51FA;<code>conda</code>&#x7BA1;&#x7406;&#x73AF;&#x5883;&#x7684;&#x5176;&#x4ED6;&#x547D;&#x4EE4;&#xFF1A;</p>
<ol>
<li><p>&#x521B;&#x5EFA;&#x7684;&#x65F6;&#x5019;&#x6307;&#x5B9A;&#x9700;&#x8981;&#x5B89;&#x88C5;&#x7684;&#x5305;&#xFF1A;</p>
<pre><code> conda create --name xxx numpy pandas
</code></pre></li>
<li><p>&#x521B;&#x5EFA;&#x7684;&#x65F6;&#x5019;&#x65E2;&#x9700;&#x8981;&#x6307;&#x5B9A;&#x5305;&#xFF0C;&#x4E5F;&#x9700;&#x8981;&#x6307;&#x5B9A;python&#x73AF;&#x5883;&#xFF1A;</p>
<pre><code> conda create --name xxx python=3.7 numpy pandas
</code></pre></li>
<li><p>&#x8FDB;&#x5165;&#x5230;&#x67D0;&#x4E2A;&#x73AF;&#x5883;</p>
<pre><code> windows: activate xxx
 mac/linux: source activate xxx
</code></pre></li>
<li><p>&#x9000;&#x51FA;&#x73AF;&#x5883;&#xFF1A;</p>
<pre><code> deactivate
</code></pre></li>
<li><p>&#x5217;&#x51FA;&#x5F53;&#x524D;&#x6240;&#x6709;&#x7684;&#x73AF;&#x5883;&#xFF1A;</p>
<pre><code> conda env list
</code></pre></li>
<li><p>&#x79FB;&#x9664;&#x67D0;&#x4E2A;&#x73AF;&#x5883;&#xFF1A;</p>
<pre><code> conda remove --name xxx --all
</code></pre></li>
<li><p>&#x73AF;&#x5883;&#x4E0B;&#x7684;&#x5305;&#x5BFC;&#x51FA;&#x548C;&#x5BFC;&#x5165;&#xFF1A;</p>
<ul>
<li>&#x5BFC;&#x51FA;&#xFF1A;<code>conda env export &gt; environment.yml</code>&#x3002;</li>
<li>&#x5BFC;&#x5165;&#xFF1A;<code>conda env create --name xxx -f environment.yml</code>&#x3002;</li>
</ul>
</li>
</ol>
<h3 id="&#x5305;&#x7BA1;&#x7406;&#xFF1A;">&#x5305;&#x7BA1;&#x7406;&#xFF1A;</h3>
<p><code>conda</code>&#x4E5F;&#x53EF;&#x4EE5;&#x7528;&#x6765;&#x7BA1;&#x7406;&#x5305;&#x3002;&#x6BD4;&#x5982;&#x6211;&#x4EEC;&#x521B;&#x5EFA;&#x5B8C;&#x4E00;&#x4E2A;&#x65B0;&#x7684;&#x73AF;&#x5883;&#x540E;&#xFF0C;&#x60F3;&#x8981;&#x5728;&#x8FD9;&#x4E2A;&#x73AF;&#x5883;&#x4E2D;&#x5B89;&#x88C5;&#x5305;&#xFF08;&#x6BD4;&#x5982;numpy&#xFF09;&#xFF0C;&#x90A3;&#x4E48;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;&#x4EE5;&#x4E0B;&#x4EE3;&#x7801;&#x6765;&#x5B9E;&#x73B0;&#xFF1A;</p>
<pre><code>activate xxx
conda install numpy
</code></pre><p>&#x4EE5;&#x4E0B;&#x518D;&#x4ECB;&#x7ECD;&#x4E00;&#x4E9B;&#x5305;&#x7BA1;&#x7406;&#x5E38;&#x7528;&#x7684;&#x547D;&#x4EE4;&#xFF1A;</p>
<ol>
<li><p>&#x5728;&#x4E0D;&#x8FDB;&#x5165;&#x67D0;&#x4E2A;&#x73AF;&#x5883;&#x4E0B;&#x76F4;&#x63A5;&#x7ED9;&#x8FD9;&#x4E2A;&#x73AF;&#x5883;&#x5B89;&#x88C5;&#x5305;&#xFF1A;</p>
<pre><code>conda install [&#x5305;&#x540D;] -n [&#x73AF;&#x5883;&#x540D;]
</code></pre></li>
<li><p>&#x5217;&#x51FA;&#x8BE5;&#x73AF;&#x5883;&#x4E0B;&#x6240;&#x6709;&#x7684;&#x5305;&#xFF1A;</p>
<pre><code> conda list
</code></pre></li>
<li><p>&#x5378;&#x8F7D;&#x67D0;&#x4E2A;&#x5305;&#xFF1A;</p>
<pre><code> conda remove [&#x5305;&#x540D;]
</code></pre></li>
<li><p>&#x8BBE;&#x7F6E;&#x5B89;&#x88C5;&#x5305;&#x7684;&#x6E90;&#xFF1A;</p>
<pre><code> conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
 conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
 conda config --set show_channel_urls yes
</code></pre></li>
</ol>

                    
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